Over 1.6 million adolescents in the United States meet criteria for substance use disorders (SUDs). Promising treatments for reducing the immediate and long-term negative effects of SUDs are available. However, deciding between outpatient, intensive outpatient, and residential services for adolescents needing treatment is complex, as it often requires a sequential, individualized approach. Those treating adolescents with SUDs must attend to the specific needs of adolescents and observe how their clients are responding to treatment and, in response, make choices about the types and duration of services they should receive. This type of sequential decision-making is imperative given the significant heterogeneity in how adolescents respond to treatment over time. In practice, these sequential decisions are often made without any empirical support. The American Society of Addiction Medicine's Patient Placement Criteria (ASAM PPC) was a major step forward in services planning for adults and adolescents but the empirical research foundations of the PPC are based primarily on adult, rather than adolescent, data [6-13]. Empirical work is needed to address this gap and evaluate decision rules that can lead to effective treatment services planning for adolescents with SUDs. The purpose of this five-year R01 study is to develop well-operationalized, empirically-supported sequences of decision rules-known as Adaptive Interventions (AIs)-to provide guidance for providers, families, and policymakers involved in making treatment services decisions for adolescent clients. AIs can improve clinical practice by guiding the placement of adolescents into the most appropriate treatment service at the appropriate time. To develop these AIs, we propose a novel, mixed-method approach. First, an iterative stakeholder engagement process will elucidate the complex issues in sequential treatment services decision-making; this formal process is a vital step for developing feasible AIs. Second, equipped with knowledge from our stakeholders, we will utilize modern statistical methods to empirically identify and then evaluate AIs using a large, observational dataset-funded by the Center for Substance Abuse Treatment (CSAT)-of over 24,000 adolescents in substance use treatment. Specifically, we aim to (1) understand how sequential decisions are made in current practice, and identify key components of feasible AIs with input from adolescent substance use providers, policymakers, researchers, and advocates; (2) empirically identify high-quality candidate AIs for adolescent clients; and (3) evaluate the relative effectiveness of the candidate AIs by examining their causal effect on relevant clinical outcomes. Identifying AIs that most effectively move youth between outpatient, intensive outpatient, and residential services is a complex but policy-relevant problem. Developing AIs from observational study data, by applying the modern methods proposed in our study will provide guidance to current practitioners and lay the foundation for subsequent experiments that can test candidate AIs in rigorous clinical trials.